Our Vision is To Make AI Explainable and Affordable.
Problem: Improve agent
productivity suggesting right
answers to customer
questions.
Solution: Collected 650
tickets and applied NLP techniques
like
stemming, lemmatization to extract
the core of the tickets. The core
sentences were classified using
Alpes algorithm to form the base
model.
This model learns incrementally
every day.
Result: 90% accuracy in
showing relevant results.
Problem: Build a background
removal tool from photos.
Solution: Using image
processing techniques we identify
the main
object in focus. The object pixel co
ordinates are identified and the
remaining pixels are converted to
white..
Result: Background was
removed properly in 90% of
cases..
Problem :
Automate generation of presentation
based on user inputs provide
prediction of what presentation
should contain and how content has
to be
arranged
Solution: Multiple prediction
module such as Chart Prediction,
Icon prediction, Diagram prediction,
free space prediction on images,
title and sub-title prediction were
and added in a work flow to achieve
the required automation
Result: Solution was
successfully delivered and its live
now for users.
Problem: To build
classification model to identify
between fraudulent/Non-fraudulent
transactions.
Solution:Using Attributes and
transactions data for remitter and
beneficiary, applying feature
engineering and domain expertise to
lower
false positives, Tuning of feature
space and achieving the best
accuracy rate from the various
detection models
Result:Reduced fraud related
costs, Relationship analysis of
fraudulent networks and collusions,
Improved data credibility, uncovered
hidden correlations.
Alpes has the fastest learning algorithm with time complexity. This will allow you to iterate training with your data fast to find the right feature set
Algorithm can perform incremental learning. When fed with new training data you don't need to run the whole training process again. This will allow your products to imbibe new data on the fly while making decisions
Clear understanding of why the system is learning or not learning. AI is not a blackbox anymore. This will allow you to fine tune your data to make your product learn better
Model size storage and compute savings of more than 20 times
Leading the research in Alpes, Dr. Kumar carries 37 years of research experience in the application of computers in the areas of industrial image processing, AI and pattern recognition, electromagnetics, fluid mechanics, and structural mechanics. He has used many numerical techniques such as Finite Element Methods (FEM), Finite Differences, Boundary Integral Equation (BIE), and minimization methods to solve various problems.
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